Method of designing products and processes

ABSTRACT

A method for use in a system for prioritizing risks associated with the design and manufacture of products involves performing a failure mode detection analysis. Functions of the product and/or of the process and the effects of undesirable events related to malfunctions are characterized, and potential failure modes and causes for the effects are identified. The features of the product/process that are responsible for the undesirable effects are determined. Severity, occurrence and detectability of the undesirable events are evaluated using specific measures designed to encourage score keeping, and risk factor numbers are computed. The resultant numbers are ranked and prioritized for a set of selected functions. Risk reduction may then be performed through engineering activities directed to remedy causes associated with the malfunctioning features. Failure mode detection analysis may be updated to continue incremental and focused improvements to the product and/or process. The method permits the capture of potential failures before they reach the field and the customer.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/198,915, filed Jul. 19, 2002, which claims the benefit of U.S.Provisional Patent Application No. 60/306,989, filed Jul. 20, 2001, bothof which are incorporated herein by reference in their entireties.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to methods for designing andmanufacturing products. In particular, the invention is related tomethods for identifying potential risks associated with design andmanufacturing of products.

2. Description of the Related Art

Product failure in the field, or in the customer's office, is usuallyone of the costliest events that a manufacturer has to manage.Conventional methods rely on various quality control techniques in anattempt to minimize the occurrence of such undesirable events. Onecommon approach is to identify all parts that comprise a product andenumerate all possible failure modes that can be ascribed to thoseparts. This approach leads to extensive evaluation of myriad parts thatmay minimally be related to the actual undesirable event. As a result, adisproportionately high level of resources are directed towards aseemingly limited number of causes that are responsible for the failureof a few part(s), and hence that of the product. A need exists,therefore, for a method that adequately directs, or focuses, engineeringor management resources on those parts that have a high risk of failing,and for prioritizing the risks.

SUMMARY OF THE INVENTION

There has been a long felt need to be able to identify potentialfailures of a product, preferably in the conceptual stages of theproduct's design, when corrective action can be taken with the leastcost. The present invention meets this need by providing a method foridentifying potential risks in the early development stages of aproduct, including the design phase and the manufacturing, or,processing phase of the product. The method employs a failure modedetection analysis (FMDA) approach, which is applicable to both theproduct and the process of making the product. Furthermore, FMDAapproach can be performed on a product or a process as well as across arange of products or processes.

In one aspect of the invention, the method involves identifying a set offunctions for the product. These functions are then prioritized in termsof the risks associated with an improper behavior, or malfunction of arespective function. A malfunction results in an undesirable effect. Theeffect may be economically characterized in two words: an active verbthat describes the undesirable effect, and a noun that describes thepart or feature of the product that could potentially malfunction. Thefeature that can lead to a potentially undesirable event in the field,therefore, is discovered in the early stages of the product development,preferably before being released for manufacturing. The disclosed methodleads naturally to the discovery of those features of the product thatare likely to have a high risk of failing, and not necessarily to allfeatures that may fail. Then the risks are prioritized.

In another aspect of an embodiment of the present invention, potentialfailure modes, and the causes for the failure modes are identified inorder to perform risk prioritization. After having listed all thepotential causes for the failure modes, a set of measures is constructedto rate the severity of the malfunction (i.e., the effect of the failureon the customer), the occurrence of failure modes (the amount ofengineering activities expanded to reduce the potential of occurrence),and the effectiveness of procedures employed for detection of the causesresponsible for the malfunction of the feature(s).

An aspect of an embodiment of the present invention involves a methodfor addressing risks associated with a product or process. A pluralityof risks is identified. A risk factor is assigned to each of the risks,the risk factors capable of being numerically ranked. The risk factorsare ranked. A subset of risks based on the ranking is selected, and therisks are addressed in the order of ranking.

An embodiment of another aspect of the invention involves a method forassigning a risk factor associated with one or more aspects of a productor process. A function associated with the product or process isidentified. The function is capable of malfunctioning, the malfunctionhaving at least one associated effect. One of the associated effects isselected. At least one potential failure mode is identified with theassociated effect and selected. At least one potential cause associatedwith the failure mode is identified and selected. For the selectedcause, an aspect associated with the function responsible for themalfunction is identified. A risk factor is then assigned to at leastone of the groups consisting of the potential cause and the potentialeffect.

In an embodiment of an aspect of the present invention, a method forrepresenting a risk assessment framework is disclosed. The riskassessment is associated with a product or a process having a pluralityof aspects. A plurality of elements is assigned for representing therisk assessment. Elements are assigned attribute, phenomenon, measure,and activity categories based on the aspects of the product. Theelements are arranged to form the representation of the risk assessment.

An embodiment of still another aspect of the present invention involvesa method, in the design of the product, for assigning levels of risk toaspects of a product to facilitate reduction of such risks. An aspect ofthe product is identified with a potential failure effect, which in turnis associated with a plurality of potential failure modes. A pluralityof causes is identified corresponding to each potential failure mode.Measures for rating severity, occurrence and detection of each one ofthe pluralities of causes are defined. Measures are used to compute aset of numerical risk factors corresponding to each one of thepluralities of potential causes. A highest risk factor of the set ofrisk factors is assigned to the corresponding one of plurality ofpotential failure modes. Highest risk factor of the set of risk factorsis assigned to the corresponding one of the plurality of potentialfailure modes. The highest risk factor numerically exceeding otherhighest risk factors is selected to represent a first representativerisk factor for the plurality of the failure modes. Risk factorscorresponding to potential causes are then summed. The potential causeshave comparably defined characteristics to represent a secondrepresentative risk factor for the defined potential cause. The firstand second representative risk factors are compared on a bar graph toyield a comparison useful in reducing risk in a prioritized manner.

An embodiment of yet another aspect of the present invention involves amethod for analyzing risk associated with a plurality of dissimilaraspects of a product or process. Whether to analyze risk associated witha particular aspect or to compare risk across the plurality of aspectsis determined. At least one descriptor of a particular aspect is definedif that particular aspect is to be analyzed. In comparing risk acrossthe plurality of aspects, a plurality of descriptors common to aspectsis defined. Depending on whether risk is being analyzed for theparticular aspect or across the plurality of aspects, a risk analysis isperformed, based on the descriptors, for the particular aspect or forthe plurality of aspects to yield a risk assessment of aspects withcommon attributes.

Still another embodiment of an aspect of the present invention involvesassessing risks across a plurality of dissimilar products or functions,or both products and functions, the risks being associated with a set ofeffects common to the products and functions. A plurality of descriptorsfor the common effects is specified. An FMDA is performed with respectto the descriptors to yield risk assessment across a set of products andfunctions.

An embodiment of an aspect of the present invention is acomputer-implemented user-interactive method for identifying particularrisks associated with at least one of a product and process. Thecomputer is in communication with a display. A failure effect for atleast one of the product and process is selected. The display displays aterm describing the failure effect. A scale of severity ratingscorresponding to failure events for one or both of the product orprocess is identified. The selected failure effect described in thedisplay is compared to the severity ratings scale. A severity rating forthe failure effect is selected. A priority rating is assigned to thefailure effect, a failure mode leading to the failure effect, and acause of the failure mode, wherein the assignment of the priority ratingis at least partly a function of a selected severity rating.

Still another computer-implemented user-interactive method affectsprioritizing risks of at least one of a product and process. Adescription of a user-identified failure effect associated with one orboth of the products and process is displayed. A severity rating for thefailure effect, an occurrence rating for one of a cause, a failure modeleading to the failure effect, and a detection rating for one of thecause and the failure mode leading to the failure effect, are received.A risk factor that is at least partly a function of at least one of theseverity rating, occurrence rating, and detection rating is calculated.The calculated risk factor can be combined with a previously calculatedrisk factor, retrieved from a database, to generate a new risk factor.The new risk factor is compared with previously stored risk factors. Atleast the risk factor corresponding to a highest risk is selected withno more five functions for one analysis cycle. A description of at leastone of a function, failure effect, failure mode, and cause, upon whichthe selected risk factor is at least partly based, is displayed.

In an embodiment of yet another aspect of the present invention,computer-readable medium is provided containing a program forinstructing a computer to execute a user-interactive method forprioritizing risks in order to address priority risks associated with atleast one of a product and process. A failure effect for at least one ofthe product and process is selected. A term describing the failureeffect is displayed. The selected failure effect is compared to a scaleof severity ratings corresponding to real failure events, each of thereal failure events differing from the others. A severity rating for thefailure effect is selected. At least one of the failure effects isprioritized. The prioritization is at least partly a function of aselected severity rating.

In an embodiment of yet another aspect of the present invention, acomputer-readable medium containing a program for instructing a computeris provided to execute a user-interactive method for prioritizing risksof at least one of a product and process. A description of auser-identified failure effect(s) associated with one or both of theproduct and process is displayed. A severity rating for the failureeffect, an occurrence rating for one of a cause and a detection ratingfor one of a cause, failure effect, and failure are received. A riskfactor that is at least partly a function of at least one of theseverity rating, occurrence rating, and detection rating is calculated.The calculated risk factor is combined with a previously calculated riskfactor, stored in a database, to generate a new risk factor. The newrisk factor is compared with previously stored risk factors. At leastthe risk factor corresponding to a highest risk is selected. Adescription of at least one of a function, failure effect, failure mode,and cause, upon which the selected risk factor is at least partly based,is displayed.

In an embodiment of yet another aspect of the present invention, acomputer-readable medium containing a program is provided forinstructing a computer to perform a user-interactive method forprioritizing risks in order to address priority risks of one of aproduct and a process. A user-entered failure effect term, describing afailure effect of one of the product and process is received. Thefailure effect term in an FMDA representation is displayed. Auser-entered failure mode term, describing a failure mode leading to thefailure effect is received. The failure mode term in the FMDArepresentation on the display is aligned horizontally with the failureeffect term and to the right of the failure effect term. A user-enteredcause term describing a cause leading to the failure mode is received.The cause term in the FMDA representation is aligned horizontally withthe failure mode term and to the right of the failure mode term on thedisplay. A scale, wherein specific engineering activities, for one ofreducing occurrence of failure modes and increasing probability ofdetection of causes, are described and assigned quantitative scores onthe display. A user-selected score in the same metric as the scale isreceived. A risk factor is calculated based on the user-selected score.The score is compared with other previously calculated risk factors, anda priority risk is identified based on the comparison of risk factors.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a flow chart illustrating steps of performing Failure ModeDetection Analysis (FMDA) for prioritizing known and unknown risks inproduct design and processes, according to the present invention.

FIG. 2 is a chart showing aspects of performing FMDA in accordance withthe present invention.

FIG. 3 is a tabular presentation of an FMDA representation showing thecolumn headings used, in an embodiment of the present invention.

FIG. 4 is a cross sectional view of a brake actuator showing variousleak paths representing various failure modes.

FIG. 5 is a Design FMDA representation of FIG. 3 applied to the brakeactuator of FIG. 4, in an embodiment of the present invention.

FIG. 6 shows an embodiment of a product Severity Scale, according to thepresent invention, as compared to AIAG scale.

FIG. 7 shows an embodiment of a product Occurrence Scale, according tothe present invention, as compared to AIAG scale.

FIG. 8 shows an embodiment of a product Detection Scale as compared toAIAG scale.

FIG. 9 shows a summary of embodiments of Design FMDA scales.

FIG. 10 shows a histogram illustrating a case history showing thesignificant variances in the distribution of Risk Factor Numbers (RFNs)as a function of the assumptions used in rating, according to thepresent invention.

FIG. 11 shows a Design FMDA used for a valve, according to the presentinvention.

FIG. 12 is a Pareto chart for the valve of FIG. 11 showing stacking ofRFNs for prioritizing high-risk effects, according to the presentinvention.

FIG. 13 is an FMDA representation showing the successive shifting ofcauses towards failure modes and failure effects as the FMDA shifts itsfocus from an assembly view to components, and further deeper intofeatures, according to the present invention.

FIG. 14 is a chart showing a generalization of FIG. 13 for performingmore extensive FMDA, according to the present invention.

FIG. 15 is a chart showing aspects of Process FMDA, according to thepresent invention.

FIG. 16 is a drawing showing a ballizing operation.

FIG. 17 is an FMDA representation as it applies to FIG. 16, according tothe present invention.

FIG. 18 shows a rating scale for the Process FMDA as applied to FIG. 16showing a scale for the end-user and another one for operations,according to the present invention.

FIG. 19 shows an occurrence rating scale, according to the presentinvention.

FIG. 20 shows a detection rating scale, according to the presentinvention.

FIG. 21 shows a detection rating scale for assembled parts, according tothe present invention.

FIG. 22 shows a table showing FMDA scales for product and processdetection and occurrence, according to the present invention.

FIG. 23 shows a Pareto chart showing the RFN sum for the ballizingoperation of FIG. 16, according to the present invention.

FIG. 24 shows a Pareto chart showing another embodiment of the stackingof RFNs for the ballizing operation of FIG. 16, according to the presentinvention.

FIG. 25 shows a comparison of AIAG Standards Occurrence scale with thatof the Process FMDA scales of the present invention.

FIG. 26 shows a comparison of AIAG Standards Detection scale with thatof the Process FMDA scales of the present invention.

FIG. 27 shows the use of an FMDA representation as a scorecard,according to the present invention.

FIG. 28 is a block diagram showing a computer system and variousassociated or compatible components for implementing the embodiments ofthe present invention.

FIG. 29 is a flow chart illustrating an embodiment for a computerimplemented FMDA that may be stored in the form of a computer program ona computer readable medium.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is directed to methods of identifying andprioritizing potential risks involving design and operation ofprocesses, in order to remedy quickly and efficiently the primary causesfor existing or potential failure modes. The methods involve asystematic approach available to all levels of an organizationresponsible for design and implementation of the product or process,from top management to personnel on the manufacturing floor.

According to the present invention, FMDA is directed to functionalaspects of a product, and/or to functional aspects of the process thatcreates the product. A product is a physical item that is created by amanufacturing process. It is an aspect of the present invention that aproduct and the process that creates the physical item, each has a setof characteristic aspects of their own. A product can be an assembly ofparts comprising sub-assemblies, components, sub-components, andfeatures such as the dimensions or material properties of the parts,each physical item being an aspect of the product. Similarly, a processthat creates the parts can be an assembly of operations comprisingprocess steps, process parameters, process controls, each being anaspect of the process. At least one of a set of product aspects iscreated by at least one of a set of process aspects. It is an aspect ofthe present invention that FMDA is conducted by identifying thefunctions of a product/process, and then arriving at the aspects thatcause malfunctioning of the product/process.

FIG. 1 shows an aspect of an embodiment of the invention. Starting withthe products and processes which may be (hereafter referred to as P/P)10 of a manufacturing organization, it is generally expected that higherlevels of management are aware of those high-risk products and processesthat are associated with failures in the field or on the manufacturingline. Those risks that are known are recorded as a list of known risks30 as shown in the same Figure. It is also possible that the severityand occurrence of a malfunction of an aspect of a product or a processare also known so that the risks are prioritized 40 based on existingknowledge in the organization. Then, a set of identified high-levelrisks is selected 50 in the order of priority established previously in40. It is possible that the causes for the high-risk failures for theP/P are also known. Appropriate action is then taken to remedy the causefor the first highest level of risk 70, and the risk list is updated 80.If there are other risks remaining in the high-risk list 90, then thecauses for the other risks are identified 60 and acted upon 70 until thelist is exhausted 100.

If, on the other hand, the level of risks are not known yet, then, asshown in another aspect of the present embodiment in FIG. 1, thosepotential risks are identified and recorded separately 110. Because ofthe unknown nature of these potential risks, and the need forexpeditious identification and remedy of these risks, a separateanalysis, called Failure Mode Detection Analysis (FMDA) 120 isperformed. FMDA can be used when a known problem is occurring to scorehow effective the actions taken to potentially reduce severity,occurrence and/or detection are in protecting the customer as well asthe shareholders. FMDA can also be used after a known problem is fixedin order to further mitigate risk associated with other potentialEffects, Failure Modes and/or Causes. However, FMDA is not limited toonly a specific type of risk analysis. The method, as disclosed in thepresent invention, can be applied to situations where an element of riskneeds further analysis. Thus, an FMDA can be performed in the design ofa product or in the manufacturing or processing of the product. FMDA,therefore, generally applies to both the product and the process. Forspecific application to design, it will be designated as DFMDA and asPFMDA when the application is to a process.

Thus, in branch 25 of FIG. 1, if the risks are known but cannot beprioritized based on existing knowledge in the organization, then FMDAcan be performed in achieving the prioritization 35. Likewise, if thefailure modes, or causes for the failure modes are not known, then amodified form of the FMDA can be performed. Reference numerals 35, 45,55, 65 and 75 show alternate branches where portions of the FMDA, asappropriate and as explained below, can be performed to resolve aparticular step before proceeding with the next step.

In branch 105 of FIG. 1, subsequent to FMDA 200, prioritization of thevarious risks is performed based on risk factors (RF) represented asrisk factor numbers (RFN) resulting from the FMDA. Then, selecting a setof high-level risks 130, implementing risk reduction by takingcorrective actions 140, and updating FMDA 150 follow, similar to thelater steps of branch 25.

FIG. 2 shows another aspect of an embodiment of the present inventionfor performing Failure Mode Detection Analysis, or, FMDA 100. FMDAcomprises seven major portions, namely, P/P characterization I, 210;failure (i.e., risk) identification II, 220, failure (risk) evaluationIII, 230; risk calculation IV, 240; risk prioritization V 250; riskreduction 260; and updating FMDA 270.

It is common practice in the industry to break down a product or aprocess into its components, or, aspects, and even to the feature level(e.g., dimension or property of an aspect) first, and then ascribe allpossible failure modes or causes for failure to each one and every partand feature. Then, various statistical and other analyses are typicallyperformed. According to the present invention, FMDA is used to firstcharacterize the function that is being performed by the P/P 213, andthe consequence or effect of a malfunction in the P/P 215. Identifying apotential failure mode 223, and a potential cause for the failure mode225, enables the identification of the feature (e.g., dimension orproperty) 227 of an aspect that is responsible for the undesirableevent. In general, there can be a plurality of causes for a potentialfailure mode, a plurality of failure modes for a potential effect, andthe order in which these are identified can also be varied withoutlimitation.

In order to assess the level of risk associated with any one of thepotential undesirable effects, failure modes, and causes (collectivelyreferred to here as “elements” of an FMDA), it is necessary to evaluatethe severity 233 and frequency 235 of occurrence and the ability todetect 237 these elements. A severity scale, an occurrence scale and adetection scale are used to perform the evaluations, as described below.Each scale has rating values, and the ratings are used to calculate arisk factor number (RFN) IV, 240. The RFNs are then used to prioritizerisks associated with corresponding undesirable events.

According to an aspect of the present invention, the elements of theFMDA of the present invention can be represented in various forms,including tables, charts, spreadsheets, or in any data structure format.For illustrative purposes, a tabular representation is shown in FIG. 3.Elements comprising function 300, part or aspect name 305, potentialeffects of failure 310, potential failure mode 315 and potential causesof failure 320, are entered as headings to the columns, preferably(though without limitation), left to right, as shown in FIG. 3. Next,severity 325, occurrence 330, detection 335, elements are entered as acategory of measures for ranking risks. This is followed by a columnheading RFN 340, under which risk factors are entered. Finally, a columnfor recording engineering activities (for addressing the risks) 345 andanother column noting actions 350 are added. It is understood that otherrelated columns may be added to the FMDA representation shown in FIG. 3.

An example of a failure mode detection analysis and the use of anembodiment of an FMDA representation according to the present inventionare illustrated in FIGS. 4 and 5. The case involves a train brake systemshown in FIG. 4, which had the undesirable effect of leaking fluid(air). Leak 360 was observed outside a boot or enclosure 365 surroundingthe various components or aspects of the braking system. The key aspectsof the braking system comprised: cylinder 370, piston 375, actuatorshaft 380, packing cup, or seal 385 and a threaded opening 390 whichaccepted a threaded pipe to introduce pressurized fluid into chamber395. In response to observation of the leak, the initial measure takenwas to design and manufacture a strengthened boot to better withstandthe pressure inside the boot. It soon became obvious, however, that thefunction of the boot was not to operate under pressure.

Knowing the function of the brake, and having characterized the failureeffect, namely, “leaking brake”, the next step, according to the presentinvention, is to identify the failure modes that can occur in the brakeassembly system shown in FIG. 4. The leak can occur in several places.Possible failure modes are: a) cracks in the walls of cylinder 370, b)cracks in the face of piston 375, c) stripped threads 370, or d)defective packing cups 385. In order to test for the failure mode, anexperiment was conducted as follows:

Leaking (“bad”) and non-leaking (“good”) brake assemblies were retrievedfrom the field. Load 397, weighing not more than 125 pounds wassuspended from actuator shaft 380, while chamber 395 was pressurized toa design pressure. (If load 397 was above 125 pounds, the shaft wouldbend). It was found that the actuator of the leaky “bad” assembliesfailed at seal 385 with a load of not more than 17 pounds, while thegood ones did not fail up to about 50 pounds of force.

Next, the packing cups were swapped between the good and bad assemblies.It was discovered that, with swapped packing cups, the bad actuatorperformed as a good actuator, while the good actuator performed as a badactuator. It was found that the outer diameter of the bad cups hadshrunk over time, thus reducing the amount of seal compression, whichpermitted fluid to leak. Further analysis showed that the plasticizerused in the cup material had to be reengineered to remedy the cause forthe failure mode.

The results of this portion of the failure mode detection analysis isshown in the embodiment of the FMDA representation of FIG. 5. First, thecharacterization of the function of the part is entered succinctly,namely, “seal pressure” in column 300. Next, the name of the aspectcharacterizing the function is entered, in column 305, as “packing cup.”In order to construct an action-oriented FMDA representation, the wordsdescribing the existing or potential effect of failure are chosen. It isan embodiment of this aspect of the invention, the effect is identifiedwith a combination of an active verb and the name of the failedattribute. In this case, two effects are observed in the field. Thefirst one is degraded braking. The active verb “reduced” and the noun“brake force” capture one of the observed effects. The other effect isrelated to the leak from the boot, and the words “ruptured boot”encompasses the undesirable effect. In the case of other undesirableevents, several observations can be reported, and they are enteredaccordingly. It is another aspect of the present invention that the verbchosen should be an action-oriented, active word, while the noun is thename of the affected element that is measurable. The combination of thewords comprise: active verb/measurable noun.

Similarly, a combination of an active verb and a “measurable” noun isused to enter the failure modes in column 315. A measurable noun is thename of an aspect that is measurable. In this example, contribution tothe failure effects is one failure mode. An expression for the failuremode is “air leaking between cylinder and packing cup.” On the otherhand, there is more than one cause for the failure mode, namely: “excessclearance,” and “packing cup outside diameter (O.D.) shrinking overtime.” These characterizations of causes are entered in column 320.

Before entering rating values for the next columns for “severity” 325,“occurrence” 330 and “detection,” a separate rating analysis isperformed, according to another aspect of the present invention.However, it is an aspect of the present invention that once the failuremodes and causes are identified with the aid of the “truncated” FMDA ofFIG. 5, it is possible to skip to columns 345 and 350 in order to takethe necessary actions quickly to remedy the undesirable event. This isbecause the severity and the occurrence of the undesirable event may beclearly apparent from the reports from the field, and there is no needfor further analysis.

In another aspect of the present invention, the severity, occurrence anddetection are rankings and ratings are accomplished in a manner that isaction-oriented. Before the rating can be accomplished, a rankingprocedure is followed. Still another aspect of the present inventioninvolves a scale for the ranking and rating. A preferred scale comprisesa range from 1.0 to 6.0. The range is further partitioned into regionsdefined by “half-points” and “full-points.” Full points are whole numberdigits, while “half-points” are fractional numbers as given by 1.0, 1.5,2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5 and 6.0. Severity, occurrence anddetection measures are first ranked using a “half-point” portions of thescale ranging from 1.5, 2.5, 3.5, 4.5 to 5.5. After the ranking has beenaccomplished, a further rating is performed in a “full-point” scaleranging from 1, 2, 3, 4, 5 to 6. It will be understood that scales withother numerical values can be also be constructed.

A ranking scale for measures Severity, Occurrence and Detection areshown in FIGS. 6, 7 and 8, respectively. In FIG. 6, an exampleconcerning an exhaust gas recirculation valve (EGR) is used to contrastthe present invention with a conventional technique in order to bringout important distinctions. The well-known AIAG technique shown in thelast column of FIGS. 6, 7 and 8 was developed by Automotive IndustryAction Group and variants of the technique are used elsewhere, such asin Failure Mode and Effects Analysis (FMEA).

Columns 400 and 410 in FIGS. 6, 7 and 8 show the ranking scale, and theranking criterion, according to the present invention. Columns 420 and430 show the conventional ranking scale and ranking criteria,respectively, used by AIAG. The half-point ranking values of the presentinvention in column 400 are to be compared with the conventionalrankings ranging from 1 to 10, and they are shown under column 420 withthe corresponding AIAG rows, 421, 422, 423, 424, 425, 426, 427, 428 and429, respectively. Higher rankings indicate higher risks and lowerrankings indicate lower risks.

The range of rating values between 1.0 and 6.0, according to an aspectof the present invention, permits an opportunity to identify criteriafor ranking the severity, occurrence and detection of undesirableevents. Furthermore, only half-points are used in rankings based on thecriteria, so that there is an additional opportunity to re-evaluate therisks, depending upon the actions taken during the interim period. If asubsequent assessment of an undesirable criterion (i.e., effect)indicates a higher risk, then the evaluation is incremented to the nexthigher full-point, or whole number. If, on the other hand, theassessment indicates a lower risk, then the evaluation is decremented tothe next lower whole number. Another aspect of the present invention isthat the whole numbers are reserved to be entered as ratings intocolumns 325, 330 and 335 of the FMDA representation of FIG. 3. Analgorithm is used to calculate a risk factor number (RFN), whish is thenentered into column 340 of FIG. 3. A rating of 1 signifies the leastrisk, a risk that is identified and acted upon during the conceptualstages of a product or a process. On the other hand, a rating of 6signifies the highest risk, such as a risk experienced by a customer inthe field.

Scales can be initially set up by gauging the relative impact of theundesirable event(s) on the customer in the field. A set of failureeffect criteria that are listed under column 410 of FIGS. 6, 7 and 8 canbe generated using methods such as selecting potential failure effectsbased on those that have previously occurred with prior generationproducts or similar products. Only the latest and most up-to-datefailure effect among the effects shown in column 410 of FIGS. 6, 7 and8, is entered into column 310 of the FMDA presentation of FIG. 3, whilethe corresponding full-point ratings are entered into columns 320, 325and 330, respectively. The rows of Items that are entered into columns300-320 in FIG. 3 constitute line items for the measure columns 325-340.

The range of scale used under AIAG in FIGS. 6, 7 and 8 is defined over1, 2, . . . 10 and is generic. Furthermore, criteria corresponding toeach value of the ratings under column 430 are generally vague, withbroad descriptors, such as “Hazardous without warning,” as shown in FIG.6. In the example involving the EGR valve shown in FIG. 6, any failureeffect constitutes a non-compliance issue and would score a 10. Thus theAIAG scale provides no resolution to rank one failure mode's relativeimportance as against another. The 1 to 4 ratings shown under column 420in FIG. 6 apply only to “fit, squeak and rattle” issues. Furthermore,since whole numbers are used in the index and the failure effects cannotbe judged against real effects that occur to similar products, poorrepeatability often results. Also, the rankings in the FMDA severityscale of FIG. 6 correspond to real and differing events, such as “ValveStuck Open” and Exhaust Gas Leak.” This is in contrast to the AIAGscale, which is based on ranking various degrees of the same event, suchas “Very low-Fit, finish, squeak” and “Minor-Fit, finish, squeak.” Thisresults in less subjective evaluation, leading to more repeatable andrepresentative ratings of failure severity.

In FIG. 7, Occurrence Scale is shown as yet another aspect of thepresent invention. The occurrence rating is an assessment of the levelof engineering activity that should be directed to reducing thepotential of occurrence of the failure effect, failure mode, and/or ofthe cause leading to the failure mode. The failure effect, failure mode,and cause are all related along a chain of events, and assessing thelikelihood of one to occur, can also provide information as to thelikelihood that the others will also occur.

Occurrence rankings have traditionally been based only on engineeringjudgment. FIG. 7 also shows a comparison of the disclosed occurrencerankings against the AIAG ranking scale. The AIAG scale is based onoccurrence rates an engineer hypothesizes could affect his design. Thisentails engineering judgment and the scores may not be specific or basedon concrete acts, such as engineering measures. In another aspect of thepresent invention, the occurrence scales are specific and are based onspecific concrete engineering measures undertaken to reduce thepotential for occurrence of a failure mode, failure effect, or cause.That is, the occurrence scale provides specific scores, corresponding towhether and what type of engineering activity was undertaken that canthereafter be used to reduce the potential for occurrence of anundesirable event. As actions are taken to reduce the likelihood ofoccurrence, the occurrence ranking is reduced. The occurrence scale usedin the FMDA of the present invention is not based merely upon engineer'shunch. Rather, they are based on specific engineering measures oractivities that reduce the potential for occurrence of the failureeffect. For each ranking in the scale, it is assumed that an action istaken based on analysis performed.

As shown in column 410 and row 401 of FIG. 7, the criterion ofengineering judgment is given the highest half point ranking of 5.5. Awhole number rating of 5 may be assigned if activity such as FEA (FiniteElement analysis) has been performed. A 6 is not based on calculationsbut on ‘I think’ type contemplative approach(es) and are implemented toidentify functions associated with the product. An FMDA is developed forthe product, a credit is given and the half point ranking is decrementedby a whole point to 4.5. If design verification is conducted, anothercredit is given and the half point ranking is further decremented to3.5.

In another aspect of the present invention, design verification isperformed. Design verification involves product assurance. In otherwords, the product features are measured to assure that they performaccording to the design. This can also involve statistically designedexperiments at chosen confidence levels to establish realistic tolerancelevels for the product features. This approach has the effect ofreducing the potential for the recurrence of the failure effect.

In yet another aspect of the present invention, validation, in additionto verification, of a specific failure mode provides another diminutionin the half point ranking to 2.5, as shown in row 409, column 410 ofFIG. 7. Validation involves subjecting the failure mode to stresstesting under different environmental conditions as a function of time.If the specific failure mode, which was conceptualized in the DesignFMDA, is validated and the product proves robust, a credit is given andthe half point ranking is reduced to 2.5 For example, in the case of theprevious illustration of the leaking train brake assembly, the newlyreengineered packing cup was baked in grease to simulate time andenvironment in the field, and the design proved robust when subjected tothe test of applying weight to the shaft of the piston. Finally, if theproduct undergoes a test in multiple environments to probe for failuremodes that cannot be conceived in advance, an additional credit is givenand the half point ranking reduces to 1.5. Finally, a score of 1 couldbe obtained with Service Monitoring. During Service Monitoring, aproduct is repurchased after use and tested until failure. Anydegradation that occurred within the product during use is capturedwithin it. If repurchased parts are much weaker than validated parts(subjected to design validation testing) an unknown environment isdegrading strength over time. Service Monitoring ensures the supplierfinds the problem before the customer does. In order to get credit forservice monitoring and achieve a lower score, all activities aboveservice monitoring must have been performed as well. If not, then thehighest ranked activity that was actually performed is what should beused.

FIG. 8 shows an embodiment of the detection rankings. A detection scorecan be given based on how likely it is to detect the failure mode, thecause leading to the failure mode, or failure effect resulting from thefailure mode, given the detection systems in place. With FMDA, thislikelihood assessment is based on levels of actual detection that areput in place.

The AIAG scale in FIG. 8 utilizes descriptions such as “absoluteuncertainty, very remote, remote, very low, low, moderate, etc.” Thepresent FMDA detection scale, on the other hand, is based on the levelof detection put in place to avoid having failures occur in the fieldand to protect the customer. “Level of detection” refers to “when andwhere” a problem is detected by the detection system that isimplemented. For example, design teams are rewarded for detectingproblems early in the design cycle. It can be said that the question of“can you detect?” protects the customer and the question “when and wherecan you detect?” protects the stockholder as well as the customer. Thatis, if the problem is detected before it affects the customer, itprotects the customer, but often, this is at a time and place along thedesign process that is costly for the designer (and therefore, thestockholder). The AIAG scale poses the question, “can you detect theproblem?”, while the FMDA detection system asks, “when and where can youdetect the problem?” Teams are credited for earlier detection systems,provided the downstream detection systems are in place. In FIG. 8,downstream detection systems are assigned the higher detection rankings,indicating greater risk, and upstream detection systems are assignedlower detection numbers for lower risk. Before any credit is taken foran upstream detection system, all downstream detection systems must bein place. (The same is true for Occurrence rankings. That is, allcredits taken are only allowed if the actions associated with the higherrankings have been taken).

A summary of the FMDA scales is shown in FIG. 9. The scales are shownboth for a product and a process using an exhaust gas recirculation(EGR) valve as an example. In the scale, the lower scores assume thatthe activity listed above them has occurred. If the activity listedabove them has not occurred, a higher number should be used. The eventdetected can be a failure mode, a cause of the failure mode, or thefailure effect resulting from occurrence of the failure mode. Asdescribed above for occurrence rankings, the cause, the failure mode andthe failure effect, are all related events in a chain. Thus, detectionof any of the events in a chain can also result in detection of otherrelated events in the chain that will occur, or that have occurred.Detection thus refers to either direct detection or indirect detection.For example, detecting a failure mode can also detect a failure effectthat has occurred, or will likely occur, as a result of the failuremode. Detecting the failure mode also detects the existence of the causethat has resulted in the failure mode. However, whether causes andfailure effects identified within an FMDA representation are those thatwill occur or have occurred as a result of the failure mode, must beverified through engineering activities, such as those described in theoccurrence scale.

Having performed the half-point rankings, and the subsequent assessmentof the latest failure effect severity, occurrence and detection, thefull-point ratings, or scores, are then entered into the respectivecolumns in the FMDA representation of FIG. 3. Next, a Risk Factor Number(RFN) is computed by using an algorithm, preferably, forming a productof the score for the severity of the failure effect, the occurrencescore and the detection score. The occurrence and detection scores usedare those reflecting the ability to detect, and the ability to expendthe necessary engineering activities to reduce the potential foroccurrence of a potential failure effect, or failure mode, or cause,that could lead to the failure effect. Thus: Risk Factor Number(RFN)=Severity Rating.times.Occurrence Rating.times.Detection Rating

The RFN is used to assess overall risk. The lowest possible RFN is 1. Ascore of 1 should be unlikely as the design team should be focused onhigh-risk effects before the FMDA work is performed. A starting valuecloser to 216 is more typical. That is, according to the presentinvention, high-risk effects are first identified before initiating theFMDA. Any number of techniques can be used to choose the effect with thehighest level of risk. These include, but not limited to, the existingknowledge base within the organization, the severity and occurrence ofthe effect, and other focused engineering activities to identify therisks (e.g., ranking and comparing the severity of effects across arange of functions). However, once initial high-risk effects are chosen,FMDA may be used to continually update the focus. The highest possibleRFN for a single effect, failure mode and cause is6.times.6.times.6=216.

Continuing with FIG. 3, column 345 to the right of the RFN column isused to record focused engineering activities that are directed to riskreduction efforts. Some of these activities include, but are not limitedto, engineering analysis techniques such as modeling, verification andvalidation. Last column 350 is used to record corrective actions takento reduce risk. Once the risk reduction is accomplished, the FMDA isupdated as shown in step 150 of FIG. 1.

With RFNs, risks are rank ordered and prioritized as shown in step 40 ofFIG. 1. Then top RFNs, preferably not exceeding five (5), are selectedin step 50. These five highest risk RFNs offer the greatest opportunityfor reducing the occurrence (and severity if the design was improved) ofthe undesirable event or effect on the customer. As one risk is rolledoff the list of the five highest ranked RFNs due to corrective actionstaken to reduce the RFN, an assessment is made (reference 100 in FIG.1), and another risk item rolls up on the list, if appropriate. It is anaspect of the present invention that improvements in the design ofproducts and processes are made in discrete steps, and not necessarilycontinuously. It is another aspect that RFNs are not the targetsthemselves to be reduced, but rather that they be used as relativeindicators to point to higher risks to be reduced.

An example for how RFNs can be misused is illustrated in FIG. 10. A teamwas given a “bogie” for RFNs using the AIAG scale between 1 and 10. Inworking with multiplication of severity, occurrence and detectionratings to obtain the RFN, the team members gave a very low rating fordetection assuming that design failure modes and causes would bedetected during validation of the product. As a result, they were ableto come up with low RFNs, thus meeting their bogies. There were: 25 RFNswith values between 1 and 5; 28 RFNs between 10 and 15; 3 RFNs between20 and 25; and, 1 RFN between 30 and 35. These 450 are plotted in FIG.10. The same team was then asked to compute new RFNs assuming that novalidation test would be performed. The results 455 are also shown inthe same Figure. Inasmuch as validation provides a detection mechanism,and not necessarily a “fix”, it was remarkable to see that in theabsence of a validation test, a failure mode such as with an RFN number140 would have probably ended up being detected by the customer letalone during earlier stages. Furthermore, using RFN as a relative,prioritization number, proper focus would have been directed to thatfailure mode. A simple validation test would have detected thatparticular failure mode.

Pareto Charts can be used effectively to establish priorities.Recognizing that the failure of a critical feature of an aspect of aproduct or process can create a chain effect, namely, that the failureof the feature can lead to the failure of a component, which in turn canlead to the failure at the system level, and hence leading to thereplacement of the product, a preventive measure at the feature levelcan result in considerable cost savings. Use of the RFNs can point tosuch “leveraging” features in a system for proper action. That is,according to the present invention, RFNs for related events can besummed together, or, stacked. As an example, if a failure mode hasseveral causes, then the RFN for that failure mode is the sum of theRFNs for all the causes as first shown in FIG. 11 and then in Paretochart in FIG. 12. In FIG. 11, the individual RFNs for an EGR valve aretabulated in the last column on the right. Two of the RFNs, 470 and 480,relate to the pintle-head material being too stiff, each RFN having avalue of 125. The sum of these 2 RFNs 490 is plotted in the Paretodiagram of FIG. 12. Similarly, the sum of the thickness related RFNs,475 and 485, is plotted 493. The stacking, according to the presentinvention, provides a means to point out the need to perform designverification and validation on these high RFN related causes.

Summing RFNs can be expanded to aspects of assemblies, subassemblies,components and features according to another aspect of the presentinvention. Illustration of this approach is shown in FIG. 13, which isan expansion of FIG. 3 where the FMDA representation is extended bothvertically and laterally. In the example shown in FIG. 13, levels areexplored starting from assembly level 302, going to component level 303,and then analyzing feature level 304 in great detail. Individual RFNsfor each effect corresponding to the “depth levels” are computed incolumn 340, as before. The representation of FIG. 13 has been extendedlaterally also to accommodate for summing of the RFNs for the failuremodes in column 341 and for summing causes in column 343. Thus,individual RFNs are computed under column 340 by forming the product ofseverity, occurrence, and detection ratings. Then, under column 341,RFNs for the failure mode are summed. It will be recalled that eachfailure mode takes the higher RFN of the causes for that failure mode,and the failure mode RFNs are summed to arrive at an RFN for thecorresponding effect/failure mode. On the other hand, under column 343,the RFNs for the causes are summed directly. As the risk analysisproceeds from the assembly level to the component level, the causes forat the assembly level become the failure mode of the component level.Accordingly, new causes are entered for the newly assigned failuremodes. At the same time, the failure modes at the assembly level becomethe effects at the component level. In other words, the phenomena ofcause, failure mode and effect shift left and downward as shown by thearrows in FIG. 13. Likewise, the RFNs for the causes shift left in thesame manner as seen under columns 341 and 343. The pattern is repeatedas the analysis deepens into the features that make up the components.

The specific example of FIG. 13 depicts the case of an assembly of twoparts, Part 1 and Part 2 in which the function of the parts is todisengage, as indicated under column 300. The aspects of the assemblyare the sub-assemblies Part 1 and Part 2. At the assembly level, theeffect of the malfunction of the functions is that the assembly isinoperable. One of the failure modes is that Part 1 runs into anotherPart 3. The potential causes for these failure modes are identified incolumn 320. And the severity of the effect, the occurrence of the cause,and the detection of any of the above are rated in columns 325, 330 and335, respectively, after proper rankings have been performed.

At the subassembly component level, the failure mode at the assemblylevel now becomes the effect. Similarly, the causes are shifted left anddownwards, as shown by the arrows emanating from column 320 andterminating under column 315. At the component level, a new set ofpotential causes are identified, and the severity for the effect at thesubassembly level, and the occurrence of the cause, and the detection ofthe cause, severity and the effect are rated as before.

At the next level of analysis, namely, at the feature level of thesubassembly, the failure modes of the subassemblies become the effect atthe feature level. The causes also shift left and downward and becomethe failure modes at the features level. The RFN's of individual causescalculated and entered into column 340. Then, the RFNs for the Failuremodes are identified. For a failure mode having more than one cause, theRFN with the larger value is selected for that failure mode. (The largerRFN will bring benefit to the customer, because more engineeringresources will be directed to minimize risks with larger RFNs). Then thesum of the RFNs for the failure modes is entered under column 341. Thesum of the individual RFNs is entered under column 343. These RFN valuesalso shift left, and downwards as shown with the set of arrows undercolumns 341 and 343, similar to the shifting of the phenomena elementson the left as presented above.

The deeper the analysis, the more detailed is the information obtained,which is valuable for identifying high-risk features. Conversely, thebroader the analysis, the less is the information. This can begeneralized to exploring risks across products and processes versuswithin a product/process. By recognizing this shifting described above,one can keep the analysis of assemblies and components (seemingly havingdissimilar characteristics) at a common level, and hence, on the samePareto chart for risk analysis. For example, assembly failure modes andcauses can be compared on the same Pareto chart with component effectsand failure modes. Thus, RFNs can be kept at the same hierarchical levelfor comparison across all common levels. However, FMDA is not performedbelow the failure mode level unless the failure mechanism is understood.For each level, FMDA representation serves as a scorecard for recordingthe RFNs before and after corrective actions are taken to reduce theoccurrence and increase detection of the risks. Also, for each level inanother aspect of the present invention, the pattern can be generalizedas shown in FIG. 14. In the Figure, F represents the Function underinvestigation; A/C, Assembly or Components; E, the Effects; F.M, thefailure modes; and C, the causes.

According to the present invention, the process FMDA, or, PFMDA, isperformed in a similar manner as for the design FMDA, or, DFMDA. It ispreferred that Design DFMDAs and PFMDAs are performed concurrently. Ifthe design is not risk reduced, then it cannot be assumed that thedesign is correct.

The steps for conducting an embodiment of Process FMDA are shown in FIG.15. The steps comprise: 1) focusing on high risk functions, operations,processes or high risk steps; 2) listing the basic functions, operationsand process steps using verb/measurable-noun logic; 3) specifying theeffect on the end user and/or a subsequent operations if the function isnot achieved; 4) listing all potential failure modes for each functionof an operation; 5) listing all potential causes of each failure mode;6) assigning a severity rating based on the effect; 7) assigning anoccurrence rating to each cause of failure; 8) assigning a detectionrating to each cause of failure; 9) calculating an RFN by multiplyingthe severity rating, occurrence rating, and detection rating together;10) combining like effect, failure modes, and causes in order to find aPareto, as discussed before; 11) list the focused engineering activitiesor current design control (process control) related to each potentialcause of failure; 12) specify corrective actions required to reduce theseverity, occurrence, or detection rating.

An illustrative application of a Process FMDA is shown in FIG. 16. Inthis application, cam lobe 500 is to be interference-fitted to tubularshaft 503. This is accomplished by forcing ball of a diameter slightlylarger than the inside diameter of the tubular shaft through the shaft.The resulting expansion creates an interference fit that interlocks thecam lobe and the shaft. Applying force 507 displaces ball 505 throughtubular shaft 503.

A Process FMDA representation as applied in this example is shown inFIG. 17. The first column, 510, describes the process being analyzed.The process being analyzed may be the overall process for creating anassembly, subassembly or part (e.g., camshaft assembly). The processbeing analyzed may also be an operation (e.g., the ballizing operation)or a step within the operation (e.g., positioning the cam-lobe prior tothe ballizing operation).

The second column, 515, shows the functions of the process expressed inactive verb/measurable noun combination. These functions are: attachcam-lobes, position cam-lobes and resist fracture.

The third column, 520, shows the effect that the customer willexperience if the function is not performed properly. The customer canbe internal and external. The final customer is known as the end user.For example, the cam-lobes would be free to rotate if the attachcam-lobe function was not preformed correctly.

The fourth column, 525, describes the failure mode. The failure mode isexpressed as a combination of an active verb/measurable noun. Physicaland technical language should be used when describing failure modes.

The cause column, 530, contains an active verb/measurable noundescription of the cause of the failure. The cause column contains thecause or causes for the failure mode. For example, the causes of aninadequate interference fit can be the ball size is too small (possiblydue to wear), the shaft inside diameter is too large, the shaft outerdiameter is too small or the cam-lobe inner diameter is too large.

The three columns that follow the cause column in a Process FMDA in FIG.17 are for Severity, Occurrence and Detection rating scores, and therating is the same as disclosed before for the Design FMDA. The severityrating is an assessment of the impact the failure mode will have on theend-user or a subsequent operation. FIG. 18 shows a set of severityscales for the ballizing operation. The worst effect that the scoringteam felt a customer could experience is ranked a 5.5. That is, often, a5.5 value is the highest rating that a current generation product underconsideration would have, for it would be the parent generation thatwould have experienced the top worst rating of 6.0. Other effects areranked at 4.5, 3.5, 2.5, and 1.5 respectively. Just as for a DesignFMDA, the team compares the failure effect they are evaluating to thesehalf points and chooses the half point their failure effect is closesttoo. Then the effects are reassessed. If the assessment indicates a lessrisky effect, then the score is decremented to the lower whole number.If higher risky effect, the half-point score is incremented to the nexthigher full-point score. For example, if a failure effect is closest to‘cam-lobe rotates’ at a half-point rating of 4.5 the team making theevaluation would consider if the effect being scored is better or worse?If better it would score a 4 and if it is worse it would score a 5. Justas for the Design FMDA embodiments disclosed previously, thismethodology of two selection criteria for scoring improves a team'srepeatability.

For Process FMDAs there are two scales, an end-user scale 570 and asubsequent operation scale 580. It is an aspect of the present inventionthat the worst of the two scores is used.

FIG. 19 shows an embodiment of Occurrence ratings for a process FMDA.Occurrence rating is an assessment of what actions have been taken toreduce the potential for occurrence. Occurrence involves the use ofscoring and occurrence rating gets lower as more process knowledge isgained. The failure effect, failure mode, and cause are all relatedalong a chain of events, and assessing the likelihood of one to occur,also provides information as to the likelihood that the others willoccur.

Engineering judgment is given the highest half point rating of 5.5. Ifprocess FMDAs are developed for the process functions, a credit is givenand the half point rating is lowered to 4.5. If verification has beenperformed, then that takes full credit, lowering it to 3.5. If thespecific failure mode, which was conceptualized in the FMDA, isvalidated and the process proves robust, a credit is given and the halfpoint rating reduces to 2.5. Finally if the process is stressed and runat rate to probe for failure modes that can't be conceived, anadditional credit is given and the half point rating reduces to 1.5. Ascore of 1 could be obtained with a stable and capable process that ismonitored and has a process control plan. Monitoring ensures thesupplier finds a problem before it effects subsequent operations.

FIG. 20 shows an embodiment of detection ratings for a detailed part.The detection score indicates how likely it is that the failure modewill be detected. The FMDA detection scale is based on the level ofdetection put in place to protect the customer. Teams are rewarded fordetecting problems early in the process. The question “when and whereyou detect” protects the subsequent customers and the stockholders. TheFMDA process poses the question, ‘where can you detect the problem’.Teams are credited for earlier detection systems provided that thedownstream detection systems are in place.

FIG. 21 shows an embodiment of detection ratings for assembled parts.The actual scale may need to be modified to suit the process being riskreduced. It is an aspect of the present invention that all FMDA scalesencompass product design as well as processes. It is another aspect thatthe invention allows for flexibility in the use of scales. This is shownin FIG. 22.

A summary of the FMDA scales is shown in FIG. 22. The lower scoresassume the activity above them has occurred. If the activity above themhas not occurred a higher number should be used.

According to the present invention, stacking RFNs is to be managedproperly for Process FMDA just as for Design FMDA. The computation of astacking number can be accomplished by performing mathematicalmanipulations of different algorithms, in addition to the preferredsumming method. Many failure modes and many causes for that failure modewill result in high RFNs. The greater the number of causes, the higheris the sum. In a preferred embodiment when verification knowledge islacking, the FMDA is not to be layered past the failure modes.Preferably, the initial scorecard is used for the leading five effectsand the corresponding failure modes. If every “what-if-what-about,”approach is used, this will result in unreasonable amount ofverification and process control activities as a result of a high RFNs.Also, false and overly inflated RFNs would yield misleading Paretocharts, and hence erroneous conclusions. Verification and processcontrol activities will reduce the number of causes to a manageable listof critical few. However, this should not be excessive as not everycause is critical. The focus should be directed to finding the criticalfew causes that matter early on and putting detection systems in placeto protect against new ones that may arise.

FIG. 23 is a Pareto chart showing the RFN sum for the ballizingoperation (See FIG. 17) wherein the RFN sums for excess interference fitand inadequate interference fit are combined, or stacked. The RFN formislocated lobes failure mode is also shown. Incorrect interference fitshould be addressed first. It is number one on the list of the leadingfive RFNs. The summing of RFNs clearly points to where the priority mustbe placed in order to remove the highest risk. This will requiremodeling work and verification and validation activities focused onobtaining the correct interference fit.

FIG. 24 is another Pareto chart for the same ballizing operation FMDArepresentation of FIG. 17. Here, the RFNs for the causes of failurerelated to inadequate and excess interference fit under the “PotentialFailure Modes” column, 525, are stacked together, in accordance with oneembodiment. This stacking at the “cause level” provides an even strongeremphasis on priority of the “interference fit” failure modes.

Design and process RFNs may be combined in a sum to provide an evenstronger Pareto chart. Then design and process engineers together canwork together to reduce risk.

The last two columns, 555 and 560, on the Process FMDA, FIG. 17, are forfocused engineering activities or current process control. Column 555specifies additional engineering activities that need to be performed inorder to reduce risk. These activities include engineering analysistechniques such as modeling, verification, and process control. The lastcolumn, 560, indicates what corrective actions were performed in orderto reduce risk. The FMDA is then updated to reflect the new lower RFNnumbers.

Similar to a Product Design FMDA, a Process FMDA serves as a scorecardrecording the RFN numbers before and after activities directed towardreducing occurrence and increasing detection. They both reduce RFN.Examples of such activities are shown in FIGS. 25 and 26. As shown inFIG. 25, to reduce occurrence, an engineer working with the process mayconduct an analysis (not requiring any physical product/process) to findpotential control parameters that impact important features. Importantfeatures can cause undesirable events to occur, such as effects offailure on customers. The control parameters of a process may be anyphysical parameter of the process for which a mechanism of controlexists. Such an analysis reduces the half point score to 4.5. Theengineer may next conduct verification. Verification entails verifyingthe cause and effect relationships between the identified potentialcontrol parameters and the undesirable events. Again, as for the designFMDA, this may require effective measurement systems, conductingstatistically designed experiments at chosen confidence levels, andestablishing realistic specifications. If such activities areundertaken, the occurrence rating can be reduced to 3.5. Validation of aspecific failure mode and characterization of the operation, required toreduce occurrence rating to 2.5, comprise confirming that the specificfailure mode is indeed attributable to the identified controlparameters. Various methods may be employed to do so and a specificexample of a product DFMDA is provided below. For the occurrence ratingto drop to 1.5, the process must be intentionally operated with variableparameters such as different operators, equipment, or settings, and runat rate, and be stable and capable.

As shown in FIG. 26, to reduce the detection rating, an undesirableevent is monitored in-house to capture the event before it reachescustomers. The test may be a destructive test such that it is only basedon sampling. Such monitoring reduces the half point score to 4.5. If animportant feature that causes or leads to the undesirable event ismonitored in house using a destructive test, the half point score isreduced to 3.5. If the same such feature is monitored 100% using anon-destructive test, then the rating drops to 2.5. If the processparameter that impacts the feature of the product is controlled, therating drops to 1.5.

According to the present invention, the product/process FMDA providesthe following benefits: it

-   -   1) is an integral part of the review process for risk assessment        and commands process characterization to determine critical        process parameters that need to be controlled; specifications        for the process parameters; and a control plan for monitoring        the processes    -   2) is focused and addresses specific failure modes on specific        operations;    -   3) provides a relative scale that allows for addressing the five        highest ranked priorities;    -   4) is a dynamic document that is continually updated using        lessons learned for new products and processes;    -   5) is an integral part of the change management process wherein        when processes or products are changed, the continually updated        FMDA can be consulted to determine or provide focus and        understanding of any high-risk impacts. Also, using FMDA, risk        is analyzed prior to allowing concessions; and    -   6) can be used as a scorecard.

FIG. 27 shows a Product DFMDA for the brake actuator of FIG. 4.Following the FMDA shown in FIG. 5 for the actuator, the representationshown there is used as a scorecard as shown in FIG. 27. It is an aspectof the present invention that results of a failure mode detectionanalysis can be recorded before the analysis, as shown in the upperrepresentation of FIG. 27. The RFN before the risk reduction was a high144. After the risk reduction analysis, the risk factor number isreduced to 16. The severity rating was kept the same. Both theoccurrence and detection measures were reduced from 5 to 2 after causefor the shrinkage of the packing cup was determined.

The embodiments of the present invention can be implemented through theuse of a computer system. FIG. 28 shows a block diagram of ageneral-purpose computer for practicing the embodiments of the presentinvention. Computer system 600 includes a central processing unit (CPU)610, display screen 620, internal system memory 630, and input/outputdevices 640. In addition, computer 600 includes receiving device 660 forreceiving and reading computer-readable media 100, such as a diskette.Although the computer-readable media 100 is represented in FIG. 28 as aCD-ROM disk, computer system 600 can employ other computer-readablemedia, including but not limited to, floppy disks, tape, flash memory,system memory 630, DVD-ROM, and hard drives. Input/output 640 can beconnected to a variety of devices, including keyboard 680 or mouse (notshown). In addition, remote devices that send and receive signals canalso communicate with computer system 600 through these input/outputs640, such as other devices within a network. The computer system alsoincludes database 670. The database is shown as being external tocomputer system 600 but may be either internal or external in accordwith various embodiments of the invention.

One embodiment of the present invention comprises a computer-readablemedium, 650 or 630, that contains instructions, or a program, fordirecting the computer 600 to carry out a computer implemented processor design FMDA, in accord with at least one of the embodiments providedabove, or variations thereof. In one embodiment, the computerimplemented FMDA includes displaying the appropriate FMDA representationfor a process or design, shown in FIGS. 3 and 17. Data entry fields areavailable for entry of information into the appropriate location in therepresentation, through keyboard 680. This information is stored in thedatabase 670. The instructions also provide for reading the data orinformation entered into the field, such as, for example, the form ofwording for functions, and suggests combinations of appropriate activeverb/nouns. Such instructions may be continually modified or updated,including continually building various selectable databases, such as adatabase of word combinations that can be suggested, or scales that canbe selected for use in providing severity, occurrence or detectionscores.

Some embodiments of the program involve calculation of RFNs andprioritization by rank order. The program can calculate and displayvalues or bar charts or Paretos for various combinations of stackedRFNs, based on related effects, failure modes, or causes. In such anembodiment, the leading five stacked RFNs can be identified and can becontinually updated by the program.

One aspect of the computer-implemented embodiment comprises displaying arepresentation wherein a user selected description of effect of failure,function, failure mode, cause, and part name are entered and displayedsimilar to the FMDA representations of FIGS. 3 and 17. The computer thendisplays as severity scale for various effects of product failure for aspecific product, whereby a user of the method can use the scale tocompare the ratings to the selected effect of product failure displayedin the representation. A field is provided for entering and displaying aselected severity rating, or score, for the user-entered effect ofproduct failure. The computer then calculates a risk prioritizationrating that is at least partly a function of the user selected severityrating. Also, the computer compares the selected effect, cause, orselected failure mode, to previously selected effects, causes, orfailure modes, stored on a data base, and combines the riskprioritization rating with previously calculated combined riskprioritization ratings for like effects and failure modes to generate anew combined risk prioritization rating. That combined rating iscompared with previously stored combined risk prioritization ratingsthat are based on similar severity scales, and the highest combined riskprioritization ratings are displayed.

In yet another embodiment of the present invention, a computer readablemedium contains a program for instructing a computer to perform auser-interactive FMDA. The program instructs the computer to receivedescriptive terms, and display them in a representation. At the sametime, the representation displays descriptions of the term types, suchas function, part name, failure effect, failure mode, and cause, to beentered in specific fields by the user. In this way, the user isinstructed to arrange term types on the display in a way correspondingto the FMDA representation. That is, the order of the representation isarranged such that terms that describe effects come before terms thatdescribe causes.

In another aspect of the embodiments, the program also instructs thecomputer to retrieve associated part names, failure modes, causes,effects, or functions, from databases developed for similar products orprocesses, and the user can select whether they are appropriate fordescribing the system being designed. The computer readable medium alsocontains instruction to display specific severity, occurrence, anddetection scales, and provide fields for the user to enter scores forthe failure modes, effects, and causes being analyzed. The computerreadable medium also provides instruction to allow the computer tocompare user-entered descriptions for functions, parts, failure effects,failure modes, and causes, to previously entered descriptions of thesame that are stored in a database. The user can specify criteria, orcriteria can be pre-programmed for selecting similar failure modes andcauses and combining RFNs for the selected similar failure modes andcauses. The criteria may be based on character comparisons for stringsof various length and patterns. Stored RFNs associated with the similarcauses and failure modes are stacked or combined. Combined RFNs are thencompared and the highest priority risks are identified and displayed(RFNs do not have to be combined if there are no like effects or failuremodes, but may still be compared against stacked RFNs). One embodimentof a computer-implemented method that can be stored on a computerreadable medium is shown in FIG. 29.

Various embodiments of the present invention can be implemented throughuse of a computer system, in various forms, such as on stand-alonecomputer systems, networks, control systems, or combinations thereof.

Although specific embodiments of the invention are described here forillustrative purposes, modifications can be made without departing fromthe spirit and scope of the invention. The various aspects of thepresent invention can be applied to virtually any product or process.Also, the teachings themselves, without direct application an actualproduct or process, encourage the application of the invention and thusthe focus of engineering efforts on high priority risks. The variousembodiments described can be combined to provide further embodiments.The described methods can omit some acts, can add other acts, and canexecute the acts in a different order than that illustrated, to achievevarious advantages of the invention.

These and other changes can be made to the invention in light of theabove detailed description. In general, in the following claims, theterms used should not be construed to limit the invention to thespecific embodiments disclosed in the specification. Accordingly, theinvention is not limited by the disclosure, but instead its scope isdetermined entirely by the following claims.

1. A method for addressing risk associated with a product or process, comprising the steps of: identifying a plurality of risks associated with the product or process; assigning a risk factor to each of the plurality of risks, the risk factors capable of being ranked; ranking the risk factors; selecting a subset of the risks based on the ranking; and addressing only those risks that have been selected.
 2. The method according to claim 1, wherein the risk factor corresponds to a function of the product or process, and wherein the addressing the risk involves attending to the function for risk reduction.
 3. The method according to claim 1, wherein the risk factor comprises a risk priority number.
 4. The method according to claim 1, wherein the ranking comprises a Pareto ranking.
 5. The method according to claim 1, wherein the subset of risks correspond to five functions.
 6. A method for assigning a risk factor associated with one or more aspects of a product or process, the method comprising the steps of: (a) identifying a function associated with the product or process, the function capable of malfunction, the malfunction having at least one associated effect; (b) selecting one of the associated effects; (c) for the selected associated effect, identifying at least one potential failure mode; (d) selecting one of the at least one potential failure mode; (e) for the selected failure mode, identifying at least one potential cause; (f) selecting one of the at least one potential cause; (g) for the selected cause, identifying a aspect associated with the function responsible for the malfunction; and (h) assigning a risk factor to least one of the groups consisting of the potential cause, the potential failure mode and the potential effect.
 7. The method according to claim 6, wherein the step of assigning a risk factor comprises determining a rating selected from the group consisting of a severity rating, an occurrence rating and a detection rating.
 8. The method according to claim 6, wherein the step of assigning a risk factor comprises the further steps of determining a severity rating, an occurrence rating and a detection rating, and computing the risk factor on the basis of the determined ratings.
 9. The method according to claim 6, wherein the risk factor is computed according to an algorithm.
 10. The method according to claim 9, wherein the algorithm comprises a product of the severity rating, the occurrence rating and the detection rating.
 11. A method for representing a risk assessment framework associated with a product or a process having plurality of aspects, the method comprising the steps of: identifying a plurality of elements for representing the risk assessment; defining attribute, phenomenon, measure, and activity categories for the elements; assigning the elements to the categories; ascribing value to the elements based on the aspects of the product; and arranging the elements to form the representation of the risk assessment framework.
 12. The method according to claim 11, wherein the plurality of elements comprise fields in a data structure.
 13. The method according to claim 11, wherein the attribute category comprises a function of an aspect of the product or of the process.
 14. The method according to claim 11, wherein the attribute category comprises the name of the aspect of the product or the process.
 15. The method according to claim 11, wherein the phenomenon category comprises a potential effect of the risk of an aspect of the product or the process.
 16. The method according to claim 11, wherein the phenomenon category comprises a potential failure mode of an aspect of the product or the process.
 17. The method according to claim 11, wherein the phenomenon category comprises a potential cause for the failure mode of an aspect of the product or the process.
 18. The method according to claim 11, wherein the measure category comprises severity, occurrence and detection rating scales.
 19. The method according to claim 18, wherein the rating scale comprises a range of rating values from 1 to
 20. The method according to claim 19, wherein the range of rating values comprise full-points 1, 2, 3, 4, 5 and
 6. 